Machine Learning for Mucisicans

Machine Learning for Mucisicans
Machine Learning for Musicians Image link: https://en.wikipedia.org/wiki/National_Youth_Wind_Orchestra_of_Great_Britain
C O N T E N T S:

KEY TOPICS

  • It gives musicians (and deep learning followers) the ability to explore completely new sounds generated by the NSynth machine learning algorithm.(More...)
  • When given musical data, machine learning algorithms can find the patterns that define each style and genre of music.(More...)
  • Deep learning is a subfield of machine learning -- a type of data analysis that uses self-learning algorithms to analyse big data, learn from it, and eventually solve a problem, provide insights, or predict an outcome.(More...)
  • In our course Sound Production in Ableton Live for Musicians and Artists, where students are learning to mix and produce music, the machine can actually provide a level of detail that is difficult for a human to replicate.(More...)

POSSIBLY USEFUL

  • Flow Machines, a project directed by Sony?s Computer Science Laboratories, has also made inroads in creating musical works with artificial intelligence.(More...)

RANKED SELECTED SOURCES

KEY TOPICS

It gives musicians (and deep learning followers) the ability to explore completely new sounds generated by the NSynth machine learning algorithm. [1] These tools for musicians, powered by the open source machine learning library Tensorflow, provide insight into the ways musicians can learn, in and out of educational and professional music facilities. [2] Musicians today are beginning to use machine learning, where computers "learn" over time by being fed large amounts of data, to create music in new and innovative ways. [3]

When given musical data, machine learning algorithms can find the patterns that define each style and genre of music. [4] At the heart of recent artificial intelligence breakthroughs are machine learning algorithms, programs that find patterns in large sets of data. [4]

An example is Jukedeck, a website that uses machine learning to create music tracks. [4] Data visualization and Six Sigma practitioner who loves reading and delving deeper into the data science and machine learning arts. [1]

It?s impossible to predict where the new sounds generated by machine learning tools might take a musician, but we're hoping they lead to even more musical experimentation and creativity. [5] It?s a machine learning algorithm that uses deep neural networks to learn the characteristics of sounds, and then create a completely new sound based on these characteristics. [5] Music Streaming : Companies are using machine learning and deep learning to recommend personalized content based on data from user activity. [6] Spotify, the largest on-demand music service in the world, has a history of pushing technological boundaries and using big data, artificial intelligence and machine learning to drive success. [7] Using machine learning, students can receive live feedback on their performance and can learn which sections of music they need to focus on practicing. [3] According to its website, LANDR reportedly uses machine learning algorithms trained on the standard steps music engineers follow to master music. [6] It focuses on machine learning tools, helping artists create art and music in new ways. [8] It then uses data science and machine learning to predict future royalty streams and pays the artist based off that information. [9] As innovators they will encounter learning experiences and even failures as they use big data, AI and machine learning to drive success. [7] The more data you have, the better the accuracy of your machine learning algorithm. [10] These systems rely heavily on supervised machine learning, and Pandora?s Music Genome Project provides the largest and most detailed corpus in the world for performing this work, spanning over 1.5 million analyzed tracks. [6] Although currently a less common application of machine learning, there are interesting possibilities that could arise from applying these methods to learning music theory. [3] Machine learning is arguably one of the most important subsets of AI because it effects all other fields within AI. In any industry, you have a pattern or a model that you know to be true, you make a prediction, and then you update your model based on the result. [10] London-based producer Hector Plimmer explored sounds generated by the NSynth machine learning algorithm. [8] While these programs generally rely on audio signal processing rather than machine learning, machine learning can easily be incorporated into sound processing. [3] Because machine learning can be used to identify specific sounds, it can be used for noise cancellation or to separate sounds into different tracks. [3] In music education, machine learning techniques can supplement numerous parts of the curriculum such as musical performance, composition, theory, and production. [3] Incorporating machine learning techniques into music education can enhance learning and provide students with a richer, more personalized experience. [3] The use of machine learning to generate music opens up a new world of opportunities for teaching in music education. [3] Incorporating machine learning into music education can enrich the learning process and add new layers to already engaging courses. [3] Popgun, for example, is a start-up developing a machine learning approach to creating original pop songs, set to be released later in 2018. [3] TechEmergence conducts direct interviews and consensus analysis with leading experts in machine learning and artificial intelligence. [6] Because machine learning techniques are designed specifically to recognize patterns, they are ideal for analyzing compositions. [3]

Summary: Machine learning can predict, with significant accuracy, whether a person is a musician or not, based on fMRI data collected while subjects listened to music. [11] I believe that the coming exciting age of technological intersections in machine learning, data analytics, electronic music production, and cognitive neuroscience technology has tremendous potential in creating rapid evolvement in truly avant-garde musical genres in years to come. [12] Existing songs make up the data behind other machine learning music applications, as well. [13] A subset of AI, machine learning employs algorithms to process huge datasets fed into computer networks, which determine patterns that allow them to process new input data. [13] The next level of machine learning, called "deep learning," stacks neural network layers -- loose approximations of a human brain -- to process more data than a human brain could. [13] More rigorous mathematical models in the fields of Artificial Intelligence and Machine Learning attempt to replicate the human neurology in algorithms to do these schema formulations, data analysis, and generative predictions even further. [12] With the advent of computers in the age of information, statistical methods of "data mining" and computational methods of "learning from data" has sprouted the fields of Machine Learning and Artificial Intelligence, relying on algorithms that replicate mankind?s ability to logically compute problems. [12] K-means clustering is an example of this unsupervised machine learning algorithm, in which data point characteristics are analyzed in an n-dimensional space and euclidian distances are measured between points. [12] On a basic level, it's fair to say that algorithms are the engine of machine learning, and data is its fuel. [13]

Here's an old paper of mine written for a Music Cognition undergrad course at Berkeley, exploring the intersection of neuroscience, machine learning, and jazz. [12] More and more, machine learning is also affecting the world of music technology. [13] We grilled the experts on what machine learning can and will do to change the game of computer music. [13] Esparza went even further with his vision for machine learning enhanced computer music. [13] One of the leaders in machine learning for music production, iZotope, has begun to address that problem with recently released updates to its flagship mixing and mastering plug-ins, Neutron 2 and Ozone 8. [13] Neutron 2's Track Assistant also uses machine learning to automatically identify the type of instrument on a track and to set an "adaptive preset specific to your audio" based on the kind of sound you're after. [13] The iZotope RX6 Advanced audio repair software uses processing based on machine learning to analyze the spectrogram of audio recordings. [13] The software uses machine learning and DSP to translate the sound from different parts of each drum into a spatial map. [13] Current audio machine learning techniques use low sampling rates like 8-16kHz, or convert the audio to a graphic such as a spectrogram. [13] Sonible uses machine learning "smart filters" on its smart:EQ+ and frei:raum plug-ins to automatically set EQ levels based on probabilities for the best frequency levels. [13] In the following lecture "Business Strategy with Machine Learning & Deep Learning" explains the changes that are needed to be more successful in business, and provides an example of business strategy modeling based on the three stages of preparation, business modeling, and model rechecking & adaptation. [14] Business with Deep Learning & Machine Learning The second module "Business with Deep Learning & Machine Learning" first focuses on various business considerations based on changes to come due to DL (Deep Learning) and ML (Machine Learning) technology in the lecture "Business Considerations in the Machine Learning Era." [14]

This correlates to supervised learning algorithms in Machine Learning, in which a system is trained on a training data set to construct it?s predictive model, which will produce classifiers or regression functions to predict subsequent data. [12] Audio applications often need to run in real-time, and the majority of machine learning algorithms in the world are not intended for real-time, according to Tlacael Esparza, co-founder of Sunhouse. [13] DURHAM Google Brain's Magenta project, which is exploring the creative potential of machine learning (ML) and artificial intelligence (AI), has developed considerably since Google announced it at Moogfest three years ago. [15] Professor Ahmed Elgammal at Rutgers University has spent five years teaching his AI program to create original artwork - once again, through machine learning. [16] Google Brain's second generation machine learning system Tensorflow, uses its Tensor processing unit (TPU), which Roberts told the Moogfest audience at the American Underground at Main in Durham, "Makes it much faster to train neural networks." [15] The creator community app MusicLinx uses successful and trendy songs plus machine learning in its Smart Songwriting feature to show writers how their songs match up to what's in demand. [13] In one of his articles on machine learning, Mike Haley, the Machine Intelligence leader at Autodesk software, said we're in the "low-hanging fruit" era, where machine-learning tools handle basic optimization and error-correction. [13] Would it get even worse when a robot could take your job? Well, besides software companies not wanting to innovate their customers out of work, most of my interviewees picture a future where better machine learning functions help their human counterparts by saving them time and helping them be more creative, rather than replacing them. [13] While the Automix functions on DJ software today are ripe for ridicule, it's easy to imagine machine learning making automixing much more natural. and tailored to each particular mix, as well as make the track selection order tasteful, smooth and informed by current trends. [13] The machine learning model was able to predict the listeners' musicianship with 77% accuracy, a result that is on a par with similar studies on participant classification with, for example, clinical populations of brain-damaged patients. [11] He also mentioned that machine learning could be used to make song recommendations for DJs based on the characteristics of their track selection history. [13] "It's your idea, and then what the computer is doing via the machine learning is generating some new possible endings for you," Eck said. [16] Artificial intelligence and machine learning permeate many aspects of your everyday life. [13] "This could allow many exciting new interactions with audio machine learning systems that no one has thought of yet," he said. [13]

If you want to learn more about using Wekinator (and you will!), I highly recommend this class called, Machine Learning for Musicians and Artists. [17] I just learned about all of this in the last 24 hours from the fantastic online course, Machine Learning for Musicians and Artists. [17]

Deep learning is a subfield of machine learning -- a type of data analysis that uses self-learning algorithms to analyse big data, learn from it, and eventually solve a problem, provide insights, or predict an outcome. [18] When it comes to the allocation of tasks in the enterprise of the future, machine learning algorithms will make valuable predictions in different departments based on business data; while deep learning will drive autonomous corporate vehicles and provide security with face recognition systems. [18]

It introduces students to the basics of machine learning while focusing specifically on techniques for applying machine learning to human gestures, music and real-time data. [19] This interdisciplinary effort draws on Music Theory, Cognitive Science, Artificial Intelligence and Machine Learning, Human Computer Interaction, Real-Time Systems, Computer Graphics and Animation, Multimedia, Programming Languages, and Signal Processing. 16 One of their project is similar to SmartMusic. [20] As marketing continues to be disrupted by technology, it will be critical for brands to get "in the zone" for peak creativity and not get lost in the tech weeds. This will be more important than ever as we enter the AI era, where machine learning can inspire human learning and we will have access to exponentially more powerful tools for us to use as we create. [21] Intel's Nervana AI Academy website provides a wealth of resources, like downloadable frameworks, AI development tools, videos and tutorials, in addition to two structured courses: Machine Learning 101 and Deep Learning 101. [19] This course provides an introduction to AI, including the history of the discipline, building an intelligent agent, machine learning algorithms and real-world applications. [19] This course provides students with practical experience using some of the leading open source machine learning tools, including R, R Studio Desktop, H2O Flow and WEKA. It was designed for students who have already taken undergraduate courses in statistics and math. [19] Note that because it is a Microsoft-sponsored course, it focuses primarily on learning Microsoft's data science and machine learning tools. [19] Comprised of Internet-scale first-party data, self-adapting predictive models and integrated AI optimization, Q?s collection of machine learning technologies continually interprets the consumer behavior graph giving brands a real-time pulse of the Internet. [21] While most of the AI and machine learning classes available are designed for aspiring data scientists and developers, this one is for people interested in the arts. [19]

An accompanying commentary discusses some of the issues that are involved with use of machine learning and data mining techniques to develop predictive models for diagnosis or prognosis of disease, and to call attention to additional requirements for developing diagnostic and prognostic algorithms that are generally useful in medicine. [22] At the technical level, machine learning and data mining algorithms are now included in numerous software packages and are very easy to use. [22] It also teaches students to use some of the more common machine learning software and digital arts tools. [19] All three datasets are readily available online, and can be easily imported into statistical programs such as R for use with their built-in machine learning or data mining tools. [22] To a novice entering the field, it is highly educational to use the tools of machine learning as provided in Microsoft Excel spreadsheets and trace their operation step by step. [22] In a recent tutorial Goldstein et al. describe the use of machine learning to predict risk of death in patients admitted to an emergency after sudden myocardial infarction, using electronic health records of 1944 patients—a data set that is nearly seven times larger than the Z-Alizadehsani dataset but not out of range of many biomedical engineering groups. [22]

Created by Deeplearning.ai, this specialization includes five courses: Neural Networks and Deep Learning; Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization; Structuring Machine Learning Projects; Convolutional Neural Networks; and Sequence Models. [19] Deep learning employs algorithms that are fundamentally more complex than those employed in machine learning. [18] If you have no previous experience with artificial intelligence or machine learning, you might find this introductory course on algorithms helpful. [19] In the 162 pages of the version presently being reviewed, Brownlee describes 11 basic machine learning algorithms and implements them in Excel spreadsheets, in a rudimentary but informative way. [22] It covers reinforcement learning in depth, including gradient-based supervised machine learning, the relationship between reinforcement learning and psychology, and how to implement 17 reinforcement learning algorithms. [19] This article is a review of the book “Master machine learning algorithms, discover how they work and implement them from scratch” (ISBN: not available, 37 USD, 163 pages) edited by Jason Brownlee published by the Author, edition, v1.10 http://MachineLearningMastery.com. [22] Master Machine Learning Algorithms can be purchased online at https://machinelearningmastery.com/master-machine-learning-algorithms/ (accessed on 03.08.2017) at modest cost ($USD 37), which also includes 17 Excel spreadsheets to illustrate the main algorithms. [22] All this calls for more extensive validation of classifiers than engineers would typically contemplate when developing machine learning algorithms. [22] Today, more and more enterprises are implementing deep and machine learning to gain a competitive edge. [18] Below we introduce MusicVAE, a machine learning model that lets us create palettes for blending and exploring musical scores. [23] Very highly rated on Udacity, this program promises to "teach you how to become a machine learning engineer, and apply predictive models to massive data sets in fields like finance, healthcare, education, and more." [19] This is where machine learning truly is like a bloodhound--the model will not stop until it finds the outcome. [21] The machine learning model was able to predict the listeners' musicianship with 77 % accuracy, a result that is on a par with similar studies on participant classification with, for example, clinical populations of brain-damaged patients. [24] It ramps up quickly, delving into statistical models, R, Python, and machine learning using Microsoft Azure services. [19] ” The authors recommended a series of practical steps to improve the reliability of machine learning models, and stress the need to test the full range of the modeling process including variable selection. [22] To illustrate potential problems in applying machine learning methods to biomedical data sets, a series of calculations were done using the Haberman data set (supplementary material). [22] It focuses on using machine learning for predictive analytics and other real-world applications. [19] In order to graduate with a master's degree, students must take 10 courses and declare a specialization in computation perception and robotics, computing systems, interactive intelligence or machine learning. [19] Designed for advanced students pursuing a graduate degree in computer science, this course assumes students have already taken courses in machine learning and intermediate statistics. [19] The follow-up to Fast.ai's introductory course, this session covers some of the more recent advances in machine learning. [19] This Kaggle challenge isn't a course per say; instead, it's a competition designed to teach novices some of the fundamentals of machine learning. [19] One of the leaders in the AI and Machine Learning field is Google. [23] The author, Jason Brownlee, aims to introduce readers to practical use of machine learning. [22] Evaluation studies should be done in concordance with professional recommendations for conducting and reporting machine learning studies for predictive use in medicine (e.g. Luo et al. 2016 ). [22] It has far too few features to permit effective use of some methods in machine learning such as pruning of decision trees. [22] Beat Blender uses MusicVAE and lets you put 4 drum beats on 4 corners of a square and then uses machine learning and latent spaces to generate two-dimensional palettes of drum beats that are morph from one dimension to the other. [23] Appendix provides examples that illustrate potential problems with machine learning that are not addressed in the reviewed book. [22] It provides a simple test case for discussions of machine learning (e.g. https://www.kaggle.com/gilsousa/haberman-s-survival ). [22] On his website ( http://machinelearningmastery.com/about/ ) Brownlee describes himself as a software developer who initially taught himself machine learning “to figure this stuff out”. [22] Unlike other introductions to machine learning, the reader does not need to buy expensive software such as Matlab or grapple with complicated software such as R and Weka which are referenced in other versions of this book. [22] Machine learning and data mining techniques can discover previously unknown regularities in data and make useful predictions. [22] Goldstein BA, Navar AM, Carter RE. Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges. [22] "I think that what we?re doing that?s different from previous attempts to apply technology and computation to art is really caring about machine learning, specifically. [25] His lectures cover linear regression and linear algebra, logistic regression, neural networks, machine learning system design, anomaly detection and much more. [19] Watched more than 4 million times, this is Caltech's introductory machine learning class as taught by Yaser Abu-Mostafa in the spring of 2012. [19] We continue with a more detailed review of the book, and conclude with a commentary on some of the larger issues that are involved in applying machine learning and data mining to biomedical problems. [22] Overall, the contribution of machine learning or data mining to medical diagnosis to date has been mixed. [22] Ivezic A. Statistics, data mining, and machine learning in astronomy, practical python guide for the analysis of survey data. [22]

By using collaborative filtering–the same machine learning technique that companies like Netflix use to suggest shows and movies you might like–Topos was able to distinguish five notable tiers for traveling acts, and then build a system that could recommend a tour schedule for the budding, or established, musician. [26] Wekinator (I used a special version from this Machine Learning for Artists and Musicians Class. [17]

Douglas Eck of Google?s Magenta project talks about how machine learning can help artists make professional-sounding (if meandering) music. [27] It allows anyone to use machine learning to build new musical instruments, gestural game controllers, computer vision or computer listening systems, and more. [17] We?re exploring this very specific direction having to do with deep neural networks and recurrent neural networks and other kinds of machine learning. [27] Launch your career in Data Science, Machine Learning, Android, iOS, and more. [28] It is really powerful and a great tool for tinkering and learning about Machine Learning. [17]

In our course Sound Production in Ableton Live for Musicians and Artists, where students are learning to mix and produce music, the machine can actually provide a level of detail that is difficult for a human to replicate. [29] Using TensorFlow, the Magenta team builds tools and interfaces that let artists and musicians use machine learning in their work. [30] Leibert: Mike, so machine learning is super-challenging in itself with having to have sufficient storage available, doing data processing to scale, and also the choice of the ecosystem of tools that you can use in order to solve machine learning problems. [31] In the age of marketing and machine learning, where data algorithms are powering our most personalized recommendations, getting in front of a captive audience on a channel as specific as Spotify is a powerful way to leverage users? strong connection to a familiar brand. [32] Certainly any company that's dealing in this space and feels that things like machine learning … and AI technologies in general are central to what they do need to be thinking multiple years in advance, to make sure that they're going to build to keep up with the data and compute volume that's needed to keep pace with the data growth. [31] Carr and Zukowski have previously created a number of experimental projects that blend music and machine learning under the name Dadabots, including a series of Soundcloud bots that automatically downloaded tracks, remixed them, and uploaded the new versions, -- like Autochiptune, which automatically remade songs in the style of a retro Gameboy. [33] The pair met as undergrads at Northeastern University during a program at Berklee College, a prominent music school in Boston, and quickly bonded over a shared interest in programmatic composition and machine learning. [33] The company has designed over 100 bots that implement various AI techniques, such as deep learning and machine learning, to assess everything from a hand-drawn image to edited sound to film. [29] Learn how Airbnb is using machine learning techniques to build a sophisticated match-making algorithm to pair guests with accommodations they'll love. [31] We design award-winning software, plug-ins, hardware, and mobile apps powered by the highest quality audio processing, machine learning, and strikingly intuitive interfaces. iZotope: the shortest path from sound to emotion. [34] "Today, advances in machine learning and neural networks have opened up new possibilities for sound generation." [30] Extract more value with machine learning, memory-driven computing, and other innovations in data and analytics. [31] When I think about the machine learning aspect, what we went through and the technical challenge we went through, it is one that I think any company is going to go through that wants to be able to apply machine learning and data essentially to build better products, and that is the early investment in collection and management of large amounts of data. [31] One thing is certain: You can't do machine learning without data. [31] We think that that's going to be a huge area that we're exploring in machine learning, AI, everything else, to create that ideal personalization experience. [31] Today, we're going to talk about how Airbnb uses machine learning to transform the travel industry. [31] What has finally, and in a big way, vaulted artificial intelligence forward is machine learning. [31] Machine learning is something that you can find at all aspects of our product experience and technology stack. [31] Ozone 8 now features machine learning technology with the introduction of Master Assistant, allowing you to reach an optimal starting point for your master in seconds. [34] Reese: Well, I just have one more quick thing about metrics, which is, are there behaviors that you can't qualitatively say this was a better experience than this? Like there's no type? And if that is the case, how does machine learning play a role in that? I mean, you have to have a success metric for it to train. [31]

POSSIBLY USEFUL

Flow Machines, a project directed by Sony?s Computer Science Laboratories, has also made inroads in creating musical works with artificial intelligence. [4] The believe AI algorithms will work in tandem with musicians and help them become better at their craft by boosting their efforts and assisting them in ways that weren?t possible before. [4] Australian startup Popgun is working on a deep learning algorithm that listens to a sequence of notes that a human musician plays and generates a sequence that could come after. [4]

This is a goldmine for the deep learning enthusiasts who are interested in the audio processing field. [1]

Today, music learning reverberates outside the classroom and into the music studio as musicians are regularly incorporating AI tools into their own musical development. [2] In the future, music teachers may give a standing ovation to the more personalized education as AI becomes a lifelong learning companion. [2] AI is still learning how to program itself to teach and understand music, too. [2]

When learning music theory, students are frequently limited by their ability to apply the rules that they are taught to new pieces of music, based on limited examples. [3] A handful of startups are helping facilitate these learning experiences with apps and tools that nurture music creation. [2] Laurie Forcier, the author of Pearson's AI report, says that "lifelong learning companions" robotic tools in the form of devices or apps could ask questions, provide encouragement, offer recommendations, and connect to online resources. [2]

The neural learning project offers a synthesizer and a note sequence generation model that interacts with human musicians. [2] Companies like Third Space Learning are already implementing platforms that offer artificially intelligent software to monitor and improve teaching. [2]

As the service continues to acquire data points, it?s using that information to train the algorithms and machines to listen to music and extrapolate insights that impact its business and the experience of listeners. [7] Machine learning-based software can also be used to analyze music across time periods or genres. [3] Although music composition is a creative process, a number of companies have created machine learning-based software to supplement the artistic experience. [3]

After creating NSynth algorithm they have come up with a machine to serve as instrument, acting as the physical interface for the NSynth algorithm. [8] As a result of this dataset, we have been able to develop incredibly rich and accurate machine listening representations." [6]

All the data musicians store in the cloud for these new technological instruments could provide valuable recordings of a music student's progress. [2] Now musicians can make music using sounds generated by the NSynth algorithm from four different source sounds. [8] Quite similar to Popgun?s team, Silverstein also emphasizes his interest in using his platform to collaborate with musicians, enhancing their ability to compose music. [6] According to Popgun?s website, in the future, the company envisions that AI will collaborate with musicians to teach them how to play different instruments faster than currently possible and to enhance music production capabilities by offering new sounds. [6] It?s an open source experimental instrument which gives musicians the ability to explore new sounds generated with the NSynth algorithm. [5] In an effort to make its mountains of data available to musicians and their managers, Spotify just launched the Spotify for Artists app that provides mobile access to analytics--everything from which playlists are generating new fans to how many streams they are getting overall. [7] It was originally launched in a web version earlier this year, but the mobile app allows musicians to access the info from the tour bus and the geographic streaming data can be instrumental to musicians and their teams to plan tours more effectively. [7] They can inspire musicians in creative and unexpected ways, and sometimes they might go on to define an entirely new musical style or genre. [5] Technology has always played a role in inspiring musicians in new and creative ways. [5] For music students and emerging musicians, artificially intelligent education technology (AIEd) can reorchestrate music education to become more supportive and creative, all while democratizing the medium and the scope musicians have for creating new songs. [2] Using the dials, musicians can select the source sounds they would like to explore between, and drag their finger across the touchscreen to navigate the new, unique sounds which combine their acoustic qualities. [5] The guitar amp gave rock musicians a new palette of sounds to play with in the form of feedback and distortion. [5] The power of human creativity can be nurtured through new, democratized tech that can help everyone, even the most novice player, become a more seasoned and aware musician. [2]

Popgun boasts the first AI that learns from human musicians, possessing skills that can complement and augment music compositions and the whim of its creators. [2] One of the nicer things about higher education: Gaining awareness of the signature styles of authors, painters, musicians even before we are told their names. [8]

Deep Reinforcement learning technology like this will be a catalyst to drive the music industry forward. [10] Launched in January 2017, Australia-based startup Popgun reportedly uses deep learning through a platform called ALICE to accompany or augment musical compositions. [6] Using deep learning, neural networks are trained on thousands songs, varying across multiple genres. [6] Spotify reportedly combines a deep learning approach complimented by a process known as "collaborative filtering." [6]

"Machine learning is advancing so rapidly, we can expect music separation to continuously improve," Wichern said. [13] "Machine learning is one approach to helping computers achieve skills humans have but we can't write simple algorithms for. [15] "Machine learning can only be as good as the database it is built upon, and this database has to be correct," he said. [13]

The schematic and formulaic nature of music, with the theoretical frameworks of harmony and theory that provide rough schematic guidelines for a musician, is suitable for supervised learning systems to predict and generate music based on quality training datasets of valid musical phrasings. [12] Such techniques, which include Markov models, neural networks, clustering, and dimensionality reduction techniques can all be applied in the context of learning and predicting musical data. [12] To create sketches or paintings, neural networks use what is called "latent space learning," which maps a data distribution. [15]

While more quality data helps to improve machine learning's results, it runs up against limitations in the music space because there is not always a "correct outcome" for an artistic creation. [13] Google?s Magenta project and Sony?s Flow Machines are both looking to use AI to compose music. [35] Basics of Deep Learning Neural Networks The module "Basics of Deep Learning Neural Networks" first focuses on explaining the technical differences of AI (Artificial Intelligence), ML (Machine Learning), and DL (Deep Learning) in the first lecture titled "What is DL (Deep Learning) and ML (Machine Learning)." [14] Deep Learning Products & Services For the course "Deep Learning for Business," the first module is "Deep Learning Products & Services," which starts with the lecture "Future Industry Evolution & Artificial Intelligence" that explains past, current, and future industry evolutions and how DL (Deep Learning) and ML (Machine Learning) technology will be used in almost every aspect of future industry in the near future. [14] Deep Learning Computing Systems & Software The third module "Deep Learning Computing Systems & Software" focuses on the most significant DL (Deep Learning) and ML (Machine Learning) systems and software. [14]

In the not-so-distant future, it's easy to imagine a machine fixing a trainwreck mix in a recorded DJ set, make acapella and instrumental tracks out of a stereo audio file, and even become a virtual bandmate in your music production. [13] However if they're willing to fork over some cash, we'll help them to plaster phrases like "cognitive symphonics" and "machine music" all over their website and then raise a ishtload of funding shortly afterwards ). [35]

A classic photograph, a revered painting … just two examples of human genius that no machine could ever match. [16] In the following lectures, the most interesting competition of human versus machine is introduced in the Google AlphaGo lecture, and in the ILSVRC (ImageNet Large Scale Visual Recognition Challenge) lecture, the results of competition between cutting edge DL systems is introduced and the winning performance for each year is compared. [14]

"The machine just take the images and it tries to learn by itself what makes good art," Elgammal said. [16]

The team uses deep learning to build an AI musician that learns from live human performances and, in theory, will be able to complement human players in real-time, practically jamming together with them. [35] Some other startups have recognized the need to offer services to the David Bowies of the world, providing tools that use AI to help musicians compose better music. [35] Let?s take a look at 11 startups that are using AI for composing music and augmenting tomorrow's musicians. [35] While would-be musicians are debating whether it?s possible for a program to write music that?s indistinguishable from a human, AI is already having a field day. [35] The study utilized functional magnetic resonance imaging (fMRI) brain data collected by Prof. Elvira Brattico?s team (previously at University of Helsinki and currently at Aarhus University) from 18 musicians and 18 non-musicians while they attentively listened to music of different genres. [11] These insights, in which regions of the brain linking to working memory and decision making have been shown to be deactivated completely, conclude that professional-level jazz musicians are not fully consciously aware of the music they produce, relying more on an intuitive sub-conscious process that is difficult to explain with a purely statistical or neurological model. [12] Six musical features, representing low-level (timbre) and high-level (rhythm and tonality) aspects of music perception, were computed from the acoustic signals, and classification into musicians and nonmusicians was performed on the musical feature and parcellated fMRI time series. [11] It knows when the chorus is coming because it can parse music in real time as another musician can. [13]

Kristin Westcott Grant, CEO of Westcott Multimedia, discusses her SXSW presentation on the topic of using data and AI to help musicians maximize fan and listener engagement, and how the currently existing data is segmented among different owners. [36] Whole brain functional magnetic resonance imaging (fMRI) data was acquired from musicians and nonmusicians during listening of three musical pieces from different genres. [11] In order to build up and refine a cognitive schema for jazz, a musician undergoes the process of repeated and long-term exposure to these musical phrases and relationships, strengthening the link associations between a musical stimuli, schema classification, and reaction response. [12] An example of clustering algorithms and jazz improvisations would be finding the unique stylistic phrasings of famous jazz musicians through their recorded solos and developing a probabilistic transition matrix for a Markov Chain to generate a new jazz solo based on these insights. [12] An example of another subconscious listening ability found in seasoned musicians is to have the intuitive ability to differentiate the playing of Charlie Parker, Coltrane, Brecker, or other jazz greats by a single note solely based on the timbre of the sound. [12]

We are focusing on understanding loops and one-shots, as these types of sounds represent the vast majority of what sound designers and musicians are dealing with. [37] Popgun?s first project was AI Alice, who can predict what a musician will play and accompanies him or her, even improvising upon the human player?s score. [35] And, Magenta makes many of its ongoing developments available publicly online and collects feedback from musicians, artists and other users to advance the project. [15] "We engage with musicians and artists to get feedback and improve them." [15] Improvisational jazz music is one of the most challenging forms of spontaneous artist creativity, and additionally much more difficult for musicians coming from a classical background. [12] These results emphasize the striking impact of musical training on our neural responses to music to the extent of discriminating musicians' brains from nonmusicians' brains despite other independent factors such as musical preference and familiarity. [11]

Examples of latent space models Magenta has developed include SketchRNN for sketches, NSynth for musical timbre, and MusicVAE a hierarchical recurrent variational autoencoder for learning latent spaces for musical scores. [15] Then the details of RNN technologies are introduced, which include S2S (Sequence to Sequence) learning, forward RNN, backward RNN, representation techniques, context based projection, and representation with attention. [14]

The deep learning systems using neural networks work differently than simple computer algorithms, Roberts explained. [15] Deep Learning Project with TensorFlow Playground The module "Deep Learning Project with TensorFlow Playground" focuses on four NN (Neural Network) design projects, where experience on designing DL (Deep Learning) NNs can be gained using a fun and powerful application called the TensorFlow Playground. [14] Deep Learning with CNN & RNN The module "Deep Learning with CNN & RNN" focuses on CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) technology that enable DL (Deep Learning). [14]

Deep learning has improved the speech recognition of the Amazon Echo neural networks faster and better than the team that created it ever expected, just by people using it more and more. [13]

The next lecture "Why is Deep Learning Popular Now?" explains the changes in recent technology and support systems that enable the DL systems to perform with amazing speed, accuracy, and reliability. [14] Even experts working in the field have a hard time explaining how deep learning actually works. [13]

Their software is aimed at filmmakers who might not have enough time or money to hire real musicians. [16] I am a software engineer and musician and had wondered if I could build something before to sort through ~2 months of raw writing and jam sessions. [37] A seasoned jazz musician accompanist will also be able to instinctively recognize the chord tone qualities of a soloist, and accompany with adapted chord harmonies on the fly in improvisation, all of which involves no conscious thought. [12] "for a computer to listen to a musician, and understand what the musician is doing as another musician would. [13]

In the last lecture of the module, NN learning based on backpropagation is introduced along with the learning method types, which include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. [14]

Learning directly from data, NSynth provides artists with intuitive control over timbre and dynamics and the ability to explore new sounds that would be difficult or impossible to produce with a hand-tuned synthesizer. [23] This album is the result of that story. 15 songs were created by artists using Flow Machines: composers, singers, musicians, producers, and sound engineers, in many musical genres (pop, electronic, ambient, and jazz). [38] A prominent feature is the capability of the A.I. algorithm to learn based on information obtained such as the computer accompaniment technology, which is capable of listening to and following a human performer so it can perform in synchrony. 5 Artificial intelligence also drives the so-called interactive composition technology, wherein a computer composes music in response to the performance of a live musician. [20] The data was collected from 18 musicians and 18 non-musicians while they attentively listened to music of different genres. [24] It is a bit like introducing elementary school students to music by teaching them to play inexpensive recorders: the lessons can instil a lifelong appreciation of music but nobody pretends to turn the kids into musicians. [22] "Your brain responses to music reveal if you're a musician or not." [39] The results underline the striking impact of musical training on our neural responses to music to the extent of discriminating musicians' brains from non-musicians' brains despite other independent factors such as musical preference and familiarity. [24]

MusicVAE is a hierarchical recurrent variational autoencoder for learning latent spaces for musical scores. [23] Policy learning allows the AI agent to learn an elaborately detailed set of instructions that shows the best possible action for a given state. [18] Sponsored by the Indian government, this series of online lectures covers the fundamentals of AI, including intelligent agents, two-player games, constraint satisfaction, knowledge representation and logic, rule-based learning, fuzzy reasoning, decision trees, neural networks and more. [19] If you only have a little bit of time to devote to learning about AI, this might be a good option for you. [19]

It covers the basics of knowledge representation, problem solving, and learning methods for artificial intelligence, and it leads into a second course on the Human Intelligence Enterprise. [19] This course covers Google's TensorFlow framework in depth, as well as unsupervised and supervised learning, visualizing and hallucinating representations, and generative models. [19]

For this Appendix, the data set was randomly divided into a learning set comprising 2/3 of the complete data (204 cases) and a test set with the remaining 102 cases. [22]

Designed for application developers and data scientists, this advanced course offers hands-on practice applying deep learning techniques to real-world enterprise use cases. [19]

I guess the best way to put it is: it?s easier to help a machine learn to solve a problem with data than to try to build the solution in." [25] While it may be interesting to spend time on all of the mechanics of how the machine learns, I feel that folks sometimes miss the point about AI. [21] OMax 28 is a software environment which learns in real-time typical features of a musician's style and plays along with him interactively, giving the flavor of a machine co-improvisation. [20]

No machine learning-based models met the inclusion criteria for acceptance in that review A 2017 study by Korley et al. assessed use of clinical risk factors (such as in the Z-Aldesani database) for diagnosing CAD as a pre-test selection tool. [22] The AI used in this album, Flow Machines, is a set of online tools. [38] Then they select a set of audio recordings that determine the sound textures of the audio stems generated by Flow Machines. [38]

Classifiers were trained and validated using three algorithms in the Matlab Statistical Toolbox (support vector machine (SVM), pruned decision trees and naive Bayes classifier). [22] The algorithms discussed include linear regression, logistic regression, discriminant analysis, classification and regression trees, Naive Bayes, k-nearest neighbours, support vector machines, decision trees. [22]

Flow Machines aims at transforming musical style into a computational object to apply to AI-generated melodies and harmonies. [20] In a typical session with Flow Machines, users first select a set of scores (lead sheets) that they want to take inspiration from. [38] We consider two examples: machine learning/data mining to diagnose coronary artery disease, and for estimating prognosis of survival from breast cancer. [22] Whether it's a brand or direct response campaign, there is no way that people can find these audiences across the media ecosystem better than a machine. [21]

In TabEditor 26 (the tiny plugin for REAPER DAW), an AI was used that solves this puzzle the same way as a musician would: trying to keep all the notes close to each other (to be possible to play) while trying to fit all the piano notes into a range that can be played simultaneously on the instrument. [20] Musicians -- and scientists -- will tell you there?s a shockingly long list of things your brain is responding to: including rhythm, tonality, repetition, but also the way the melody develops, so it?s not exactly the same thing you heard a few bars before. [25] Working with musicians, I set up and operated the complex recording equipment that's used to capture, shape and mix the sounds of an album. [21] After working with many artists, I noted there were two kinds of musician: those who wanted to spend more time sitting behind the workstation pulling the knobs and levers until something was "perfect," and those who just wanted to play and be "in the zone." [21] Musicians and composers have mostly lacked a similar device for exploring and mixing musical ideas, but we are hoping to change that. [23] StarPlay is also a music education software that allows the user to practice by performing with professional musicians, bands and orchestras. [20] Special thanks to the musicians who supported us during the entire project: ALB, Barbara Carlotti, Black Devil Disco Club, Busy P, Catastrophe, Dominique Dalcan, King Doudou, Fady Farah, Jack Flaag, Arnaud Fleurent-Didier, Forever Pavot, Cine Garcia, House de Racket, Krampf, Kumisolo, Lescop, Nae New Beaters, D Nodey, Arthur Philippot, Michael Ponton. [38] More often than not, the musicians who focused on the playing had the best takes and achieved the best results. [21]

SmartMusic is an interactive, computer-based practice tool for musicians. [20]

If we take human face recognition as an example, a deep learning algorithm will not only be able to discern one face from another, but detect differences connected to the smallest details, like pores or wrinkles. [18] Designed for programmers with at least a year of experience, this class focuses on how to use deep learning to solve coding problems. [19] Join us, as we take a closer look at deep learning without going to the neighboring territories of mathematics and software engineering. [18] This is the first course in a three-course program that delves into deep learning. [19] Deep learning is set to take us to a technologically advanced, automated future of self-driving cars and robotic assistants. [18]

Taught by Sergey Levine, this course covers topics like imitation learning, policy gradients, model-based reinforcement learning and more. [19] That?s something that people are starting to do with a technique called reinforcement learning. [25] A reinforcement learning agent has to decide how to act to perform a task through the process of trial and error in a dedicated training environment. [18]

They were all showing that these musicians are learning from feedback they?re getting from peers and from crowds, but also other things that are happening with other artists. [27] Or, musicians who are still learning an instrument could easily focus on a specific part of a song they?re trying to master. [40] Using a deep learning neural network that was trained by analyzing over 60 hours of video featuring musicians playing instruments, the software is able to identify over 20 different instruments without being told what they are, and then automatically isolate the sound of one of them from the video?s audio. [40] It's a complete and friendly guide for programmers, artists, scientists, engineers, musicians, and anyone else who wants to understand and use deep learning. [41]

Computational approaches to music composition and style imitation have engaged musicians, music scholars, and computer scientists since the early days of computing. [42] One major factor influences where musicians travel-and it?s not their musical genre. [26] Musicians choose source sounds they would like to explore, and drag their finger across the touchscreen to combine acoustic qualities. [43] The way our most popular musicians tour looks virtually identical on a map, whether you’re talking about Bon Jovi, Rihanna, Billy Joel, Kanye West, or Trans Siberian orchestra. [26]

Think of it as style transfer for dancing, a deep learning based algorithm that can convincingly show a real person mirroring the moves of their favorite dancers. [44] We can set those parameters vis-vis the feedback, using reinforcement learning, and we?re working on that, too. [27]

RANKED SELECTED SOURCES(44 source documents arranged by frequency of occurrence in the above report)

1. (29) How Machine Learning Will Affect DJing + Music Production - DJ TechTools

2. (29) Machine learning and medicine: book review and commentary

3. (27) 35 Artificial Intelligence Courses - Datamation

4. (14) The Neuroscience and Machine Learning perspectives of Jazz Improvisation | LinkedIn

5. (14) How Machine Learning can Enhance Music Education | Getting Smart

6. (12) AI could be the future maestro of music education | VentureBeat

7. (11) Reviews for Deep Learning for Business from Coursera | Class Central

8. (11) Stack That podcast: Machine learning and Airbnb's algorithms | HPE

9. (10) Musical Artificial Intelligence - 6 Applications of AI for Audio

10. (9) What is deep learning? - AI Applications - Intellectsoft Blog

11. (8) Creative AI: At age 3, Google Magenta project gives musicians and artists tools | WRAL TechWire

12. (8) Are you 'in the zone' for peak creativity? | Quantcast - Ad Age

13. (7) Music and artificial intelligence - Wikipedia

14. (7) 11 Startups Using AI to Compose Music - Nanalyze

15. (7) Making music using new sounds generated with machine learning

16. (6) Your Brain Responses to Music Reveal if You Are a Musician or Not - Neuroscience News

17. (6) Google Magenta-Making Music with MIDI and Machine Learning -

18. (6) Can artificial intelligence beat musicians at their craft?

19. (5) Computer creativity: When AI turns its gaze to art - CBS News

20. (5) Using Machine Learning and YOUR FACE to Control Scratch: 21 Steps (with Pictures)

21. (5) About Hello World • SKYGGE

22. (5) The Amazing Ways Spotify Uses Big Data, AI And Machine Learning To Drive Business Success

23. (5) Machine learning drives NSynth Super's new sounds of music

24. (4) Are Computers Becoming Better at Composing Music than Humans? | KQED Arts

25. (4) Why Google?s AI Can Write Beautiful Songs but Still Can?t Tell a Joke - MIT Technology Review

26. (3) Google is making Music with Machine Learning - and has released the code on GitHub

27. (3) How To Think About Artificial Intelligence In The Music Industry

28. (3) Neural Responses To Music Show If You?re A Musician Or Not

29. (3) A Fascinating Look At How Musicians Tour The U.S.

30. (2) Grading with AI: How Kadenze Powers Its Online Fine Arts Courses | eLearningInside News

31. (2) Invent new sounds with Googles NSynth Super - Raspberry Pi

32. (2) This frostbitten black metal album was created by an artificial intelligence | The Outline

33. (2) iZotope Ushers in the Next Evolution of Mixing and Mastering with Ozone 8 and Neutron 2

34. (2) Sononym: Machine Learning Sample Organizer for Musicians : WeAreTheMusicMakers

35. (2) https://gizmodo.com/mits-new-ai-powered-software-can-extract-individual-ins-1827372032

36. (1) Artists, Musicians, Writers, are you doing Data, Development, and Digital Marketing? | Udacity

37. (1) Spotify Now Sells Beauty Products - Backed By Your Favorite Musicsions

38. (1) AI for musicians: Improving fan engagement - Video | ZDNet

39. (1) Your brain responses to music reveal if you're a musician or not -- ScienceDaily

40. (1) Deep Learning From Basics to Practice | Andrew Glassner

41. (1) Machine Learning and Music Generation (Hardback) - Routledge

42. (1) Machine Learning Drives NSynth Super's New Sounds of Music | News | Communications of the ACM

43. (1) Machine Learning & Artificial Intelligence | NVIDIA Developer

44. (1) The Music Fund wants to use AI to generate more royalties for musicians | VentureBeat

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